| A Semi-Structured Document Model for Text Mining |
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| 作者姓名： | 杨建武 陈晓鸥 |
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| 作者单位： | [1]NationalKeyLaboratoryforTextProcessing,InstituteofComputerScienceandTechnologyPekingUniversity,Beijing100871,P.R.China [2]NationalKeyLaboratoryforTextProcessing,InstituteofComputerScienceandTechnologyPek |
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| 基金项目： | This research is supported by National Technology Innovation Project and Peking University Graduate Student Development Foundation as one of doctoral dissertation's innovative research |
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| 摘 要： | A semi-structured document has more structured information compared to an ordinary document,and the relation among semi-structured documents can be fully utilized.In order to take advantage of the structure and link information in a semi-structured document for better mining,a structured link vector model (SLVM) is presented in this paper,where a vector represents a document,and vectors‘ elements are determined by terms,document structure and neighboring documents.Text mining based on SLVM is described in the procedure of K-means for briefness and clarity:calculating document similarity and calculating cluster center.The clustering based on SLVM performs significantly better than that based on a conventional vector space model in the experiments,and its F value increases from 0.65-0.73 to 0.82-0.86.
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| 关 键 词： | HTML语言 XML语言 半结构文件模型 版本开采 结构信息 |
A semi-structured document model for text mining |
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| Jianwu Yang,Xiaoou Chen.A Semi-Structured Document Model for Text Mining[J].Journal of Computer Science and Technology,2002,17(5):0-0. |
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| Authors: | Jianwu Yang Xiaoou Chen |
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| Affiliation: | (1) National Key Laboratory for Text Processing, Institute of Computer Science and Technology, Peking University, 100871 Beijing, P.R. China |
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| Abstract: | A semi-structured document has more structured information compared to an ordinary document, and the relation among semi-structured documents can be fully utilized. In order to take advantage of the structure and link information in a semi-structured document for better mining, a structured link vector model (SLVM) is presented in this paper, where a vector represents a document, and vectors' elements are determined by terms, document structure and neighboring documents. Text mining based on SLVM is described in the procedure of K-means for briefness and clarity: calculating document similarity and calculating cluster center. The clustering based on SLVM performs significantly better than that based on a conventional vector space model in the experiments, and its F value increases from 0.65-0.73 to 0.82-0.86. |
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| Keywords: | semi-structured document XML text mining vector space model structured link vector model |
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